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@compfu@mograph.social
2025-12-23 08:47:58

I've set up a #TriliumNotes server to test it as a replacement for my job-related Trello. Phew... I've rarely seen an app that looks so daunting when you first start it up. Dozens of buttons, menus, features... and on first start it wants a password but you can't use it with multiple users. You need multiple servers for that or encrypt a set of private notes.
Trilium integra…

Trilium Notes
Trilium is an open-source solution for note-taking and personal knowledge bases. Use it locally or sync with your own server to access notes anywhere.

@arXiv_csLG_bot@mastoxiv.page
2025-12-22 13:54:55

Replaced article(s) found for cs.LG. arxiv.org/list/cs.LG/new
[4/5]:
- Sample, Don't Search: Rethinking Test-Time Alignment for Language Models
Gon\c{c}alo Faria, Noah A. Smith
arxiv.org/abs/2504.03790 mastoxiv.page/@arXiv_csCL_bot/
- A Survey on Archetypal Analysis
Aleix Alcacer, Irene Epifanio, Sebastian Mair, Morten M{\o}rup
arxiv.org/abs/2504.12392 mastoxiv.page/@arXiv_statME_bo
- The Stochastic Occupation Kernel (SOCK) Method for Learning Stochastic Differential Equations
Michael L. Wells, Kamel Lahouel, Bruno Jedynak
arxiv.org/abs/2505.11622 mastoxiv.page/@arXiv_statML_bo
- BOLT: Block-Orthonormal Lanczos for Trace estimation of matrix functions
Kingsley Yeon, Promit Ghosal, Mihai Anitescu
arxiv.org/abs/2505.12289 mastoxiv.page/@arXiv_mathNA_bo
- Clustering and Pruning in Causal Data Fusion
Otto Tabell, Santtu Tikka, Juha Karvanen
arxiv.org/abs/2505.15215 mastoxiv.page/@arXiv_statML_bo
- On the performance of multi-fidelity and reduced-dimensional neural emulators for inference of ph...
Chloe H. Choi, Andrea Zanoni, Daniele E. Schiavazzi, Alison L. Marsden
arxiv.org/abs/2506.11683 mastoxiv.page/@arXiv_statML_bo
- Beyond Force Metrics: Pre-Training MLFFs for Stable MD Simulations
Maheshwari, Tang, Ock, Kolluru, Farimani, Kitchin
arxiv.org/abs/2506.14850 mastoxiv.page/@arXiv_physicsch
- Quantifying Uncertainty in the Presence of Distribution Shifts
Yuli Slavutsky, David M. Blei
arxiv.org/abs/2506.18283 mastoxiv.page/@arXiv_statML_bo
- ZKPROV: A Zero-Knowledge Approach to Dataset Provenance for Large Language Models
Mina Namazi, Alexander Nemecek, Erman Ayday
arxiv.org/abs/2506.20915 mastoxiv.page/@arXiv_csCR_bot/
- SpecCLIP: Aligning and Translating Spectroscopic Measurements for Stars
Zhao, Huang, Xue, Kong, Liu, Tang, Beers, Ting, Luo
arxiv.org/abs/2507.01939 mastoxiv.page/@arXiv_astrophIM
- Towards Facilitated Fairness Assessment of AI-based Skin Lesion Classifiers Through GenAI-based I...
Ko Watanabe, Stanislav Frolov, Aya Hassan, David Dembinsky, Adriano Lucieri, Andreas Dengel
arxiv.org/abs/2507.17860 mastoxiv.page/@arXiv_csCV_bot/
- PASS: Probabilistic Agentic Supernet Sampling for Interpretable and Adaptive Chest X-Ray Reasoning
Yushi Feng, Junye Du, Yingying Hong, Qifan Wang, Lequan Yu
arxiv.org/abs/2508.10501 mastoxiv.page/@arXiv_csAI_bot/
- Unified Acoustic Representations for Screening Neurological and Respiratory Pathologies from Voice
Ran Piao, Yuan Lu, Hareld Kemps, Tong Xia, Aaqib Saeed
arxiv.org/abs/2508.20717 mastoxiv.page/@arXiv_csSD_bot/
- Machine Learning-Driven Predictive Resource Management in Complex Science Workflows
Tasnuva Chowdhury, et al.
arxiv.org/abs/2509.11512 mastoxiv.page/@arXiv_csDC_bot/
- MatchFixAgent: Language-Agnostic Autonomous Repository-Level Code Translation Validation and Repair
Ali Reza Ibrahimzada, Brandon Paulsen, Reyhaneh Jabbarvand, Joey Dodds, Daniel Kroening
arxiv.org/abs/2509.16187 mastoxiv.page/@arXiv_csSE_bot/
- Automated Machine Learning Pipeline: Large Language Models-Assisted Automated Dataset Generation ...
Adam Lahouari, Jutta Rogal, Mark E. Tuckerman
arxiv.org/abs/2509.21647 mastoxiv.page/@arXiv_condmatmt
- Quantifying the Impact of Structured Output Format on Large Language Models through Causal Inference
Han Yuan, Yue Zhao, Li Zhang, Wuqiong Luo, Zheng Ma
arxiv.org/abs/2509.21791 mastoxiv.page/@arXiv_csCL_bot/
- The Generation Phases of Flow Matching: a Denoising Perspective
Anne Gagneux, S\'egol\`ene Martin, R\'emi Gribonval, Mathurin Massias
arxiv.org/abs/2510.24830 mastoxiv.page/@arXiv_csCV_bot/
- Data-driven uncertainty-aware seakeeping prediction of the Delft 372 catamaran using ensemble Han...
Giorgio Palma, Andrea Serani, Matteo Diez
arxiv.org/abs/2511.04461 mastoxiv.page/@arXiv_eessSY_bo
- Generalized infinite dimensional Alpha-Procrustes based geometries
Salvish Goomanee, Andi Han, Pratik Jawanpuria, Bamdev Mishra
arxiv.org/abs/2511.09801 mastoxiv.page/@arXiv_statML_bo
toXiv_bot_toot

@Techmeme@techhub.social
2026-01-19 14:15:51

Sources: internal Google data shows Gemini API calls surged from 35B in March 2025 to 85B in August 2025; Google says Gemini Enterprise hit 8M subscribers (Erin Woo/The Information)
theinformation.com/articles/go

Google’s Gemini Sees Skyrocketing Business Sales
Google’s improvements to its Gemini AI models are boosting the company’s top line. Over the past year, Google’s business selling access to its Gemini AI models has skyrocketed, reflecting the improving quality of those models, according to three people with knowledge of Gemini’s sales. That’s ...

@pbloem@sigmoid.social
2025-12-09 16:35:29

This has allowed me to make clear something that's been at the back of my mind. Something that is at the heart of so much blind stupidity in big tech.
It's the assumption that we will change one thing and all else will be the same.
In this case, we will fire lots and lots of employees all over the world and we will make lots of profit. We're smart enough to make the AI, and we're dumb enough to think that there will only be one consequence. 1/n

They argue that genAI won’t produce sufficient revenue from consumers to pay back the current investment frenzy. I mean, they’re right, it won’t, but that’s not what the investors are buying. They’re buying the promise, not of more revenue, but of higher profits that happen when tens of millions of knowledge workers are replaced by (presumably-cheaper) genAI. ¶ I wonder who, after the loss of those tens of millions of high-paid jobs, are going to be the consumers who’ll buy the goods that’ll d…
@tante@tldr.nettime.org
2025-12-15 17:00:05

"Although Ecosia is a not-for-profit, they seem to be re-enacting the for-profit erosion of human knowledge and the active murder of the open web currently being orchestrated by Google and the other tech giants."
(Original title: Ecosia’s “Green AI”: false hope for corrosive tech)

Ecosia’s “Green AI”: false hope for corrosive tech
Why replace human work with fossil-fuel-slurping mistake generators? Ecosia’s “Green AI” is selling false hope for bad tech

@arXiv_csLG_bot@mastoxiv.page
2025-12-22 13:54:45

Replaced article(s) found for cs.LG. arxiv.org/list/cs.LG/new
[3/5]:
- Look-Ahead Reasoning on Learning Platforms
Haiqing Zhu, Tijana Zrnic, Celestine Mendler-D\"unner
arxiv.org/abs/2511.14745 mastoxiv.page/@arXiv_csLG_bot/
- Deep Gaussian Process Proximal Policy Optimization
Matthijs van der Lende, Juan Cardenas-Cartagena
arxiv.org/abs/2511.18214 mastoxiv.page/@arXiv_csLG_bot/
- Spectral Concentration at the Edge of Stability: Information Geometry of Kernel Associative Memory
Akira Tamamori
arxiv.org/abs/2511.23083 mastoxiv.page/@arXiv_csLG_bot/
- xGR: Efficient Generative Recommendation Serving at Scale
Sun, Liu, Zhang, Wu, Yang, Liang, Li, Ma, Liang, Ren, Zhang, Liu, Zhang, Qian, Yang
arxiv.org/abs/2512.11529 mastoxiv.page/@arXiv_csLG_bot/
- Credit Risk Estimation with Non-Financial Features: Evidence from a Synthetic Istanbul Dataset
Atalay Denknalbant, Emre Sezdi, Zeki Furkan Kutlu, Polat Goktas
arxiv.org/abs/2512.12783 mastoxiv.page/@arXiv_csLG_bot/
- The Semantic Illusion: Certified Limits of Embedding-Based Hallucination Detection in RAG Systems
Debu Sinha
arxiv.org/abs/2512.15068 mastoxiv.page/@arXiv_csLG_bot/
- Towards Reproducibility in Predictive Process Mining: SPICE -- A Deep Learning Library
Stritzel, H\"uhnerbein, Rauch, Zarate, Fleischmann, Buck, Lischka, Frey
arxiv.org/abs/2512.16715 mastoxiv.page/@arXiv_csLG_bot/
- Differentially private Bayesian tests
Abhisek Chakraborty, Saptati Datta
arxiv.org/abs/2401.15502 mastoxiv.page/@arXiv_statML_bo
- SCAFFLSA: Taming Heterogeneity in Federated Linear Stochastic Approximation and TD Learning
Paul Mangold, Sergey Samsonov, Safwan Labbi, Ilya Levin, Reda Alami, Alexey Naumov, Eric Moulines
arxiv.org/abs/2402.04114
- Adjusting Model Size in Continual Gaussian Processes: How Big is Big Enough?
Guiomar Pescador-Barrios, Sarah Filippi, Mark van der Wilk
arxiv.org/abs/2408.07588 mastoxiv.page/@arXiv_statML_bo
- Non-Perturbative Trivializing Flows for Lattice Gauge Theories
Mathis Gerdes, Pim de Haan, Roberto Bondesan, Miranda C. N. Cheng
arxiv.org/abs/2410.13161 mastoxiv.page/@arXiv_heplat_bo
- Dynamic PET Image Prediction Using a Network Combining Reversible and Irreversible Modules
Sun, Zhang, Xia, Sun, Chen, Yang, Liu, Zhu, Liu
arxiv.org/abs/2410.22674 mastoxiv.page/@arXiv_eessIV_bo
- Targeted Learning for Variable Importance
Xiaohan Wang, Yunzhe Zhou, Giles Hooker
arxiv.org/abs/2411.02221 mastoxiv.page/@arXiv_statML_bo
- Refined Analysis of Federated Averaging and Federated Richardson-Romberg
Paul Mangold, Alain Durmus, Aymeric Dieuleveut, Sergey Samsonov, Eric Moulines
arxiv.org/abs/2412.01389 mastoxiv.page/@arXiv_statML_bo
- Embedding-Driven Data Distillation for 360-Degree IQA With Residual-Aware Refinement
Abderrezzaq Sendjasni, Seif-Eddine Benkabou, Mohamed-Chaker Larabi
arxiv.org/abs/2412.12667 mastoxiv.page/@arXiv_csCV_bot/
- 3D Cell Oversegmentation Correction via Geo-Wasserstein Divergence
Peter Chen, Bryan Chang, Olivia A Creasey, Julie Beth Sneddon, Zev J Gartner, Yining Liu
arxiv.org/abs/2502.01890 mastoxiv.page/@arXiv_csCV_bot/
- DHP: Discrete Hierarchical Planning for Hierarchical Reinforcement Learning Agents
Shashank Sharma, Janina Hoffmann, Vinay Namboodiri
arxiv.org/abs/2502.01956 mastoxiv.page/@arXiv_csRO_bot/
- Foundation for unbiased cross-validation of spatio-temporal models for species distribution modeling
Diana Koldasbayeva, Alexey Zaytsev
arxiv.org/abs/2502.03480
- GraphCompNet: A Position-Aware Model for Predicting and Compensating Shape Deviations in 3D Printing
Juheon Lee (Rachel), Lei (Rachel), Chen, Juan Carlos Catana, Hui Wang, Jun Zeng
arxiv.org/abs/2502.09652 mastoxiv.page/@arXiv_csCV_bot/
- LookAhead Tuning: Safer Language Models via Partial Answer Previews
Liu, Wang, Luo, Yuan, Sun, Liang, Zhang, Zhou, Hooi, Deng
arxiv.org/abs/2503.19041 mastoxiv.page/@arXiv_csCL_bot/
- Constraint-based causal discovery with tiered background knowledge and latent variables in single...
Christine W. Bang, Vanessa Didelez
arxiv.org/abs/2503.21526 mastoxiv.page/@arXiv_statML_bo
toXiv_bot_toot

@keithjgrant@front-end.social
2025-12-16 18:34:32

Silksong is the best game I’ve played in years. I would probably say in decades if it weren't for Outer Wilds, which skews the results. And no, it’s not too difficult. It _is_ difficult, but Team Cherry can’t change that. An easy mode would rob this game of its soul.
This game is almost a "Metroidbrainia", where the only way to progress is knowledge; knowledge of the enemies attack patterns and the reflexes to deal with them. If you could just hack and slash your way…

@alexanderadam@ruby.social
2026-01-09 23:38:59

So there's a #Mastodon instance for people that are into #math called mathstodon… and that instance allows users to use #LaTeX.
The

Terence Tao (@tao@mathstodon.xyz)
Recently, the application of AI tools to Erdos problems passed a milestone: an Erdos problem (#728 https://www.erdosproblems.com/728) was solved more or less autonomously by AI (after some feedback from an initial attempt), in the spirit of the problem (as reconstructed by the Erdos problem website community), with the result (to the best of our knowledge) not replicated in existing literature (although similar results proven by similar methods were located). This is a demonstration of the gen…

@arXiv_csLG_bot@mastoxiv.page
2025-12-22 13:54:35

Replaced article(s) found for cs.LG. arxiv.org/list/cs.LG/new
[2/5]:
- The Diffusion Duality
Sahoo, Deschenaux, Gokaslan, Wang, Chiu, Kuleshov
arxiv.org/abs/2506.10892 mastoxiv.page/@arXiv_csLG_bot/
- Multimodal Representation Learning and Fusion
Jin, Ge, Xie, Luo, Song, Bi, Liang, Guan, Yeong, Song, Hao
arxiv.org/abs/2506.20494 mastoxiv.page/@arXiv_csLG_bot/
- The kernel of graph indices for vector search
Mariano Tepper, Ted Willke
arxiv.org/abs/2506.20584 mastoxiv.page/@arXiv_csLG_bot/
- OptScale: Probabilistic Optimality for Inference-time Scaling
Youkang Wang, Jian Wang, Rubing Chen, Xiao-Yong Wei
arxiv.org/abs/2506.22376 mastoxiv.page/@arXiv_csLG_bot/
- Boosting Revisited: Benchmarking and Advancing LP-Based Ensemble Methods
Fabian Akkerman, Julien Ferry, Christian Artigues, Emmanuel Hebrard, Thibaut Vidal
arxiv.org/abs/2507.18242 mastoxiv.page/@arXiv_csLG_bot/
- MolMark: Safeguarding Molecular Structures through Learnable Atom-Level Watermarking
Runwen Hu, Peilin Chen, Keyan Ding, Shiqi Wang
arxiv.org/abs/2508.17702 mastoxiv.page/@arXiv_csLG_bot/
- Dual-Distilled Heterogeneous Federated Learning with Adaptive Margins for Trainable Global Protot...
Fatema Siddika, Md Anwar Hossen, Wensheng Zhang, Anuj Sharma, Juan Pablo Mu\~noz, Ali Jannesari
arxiv.org/abs/2508.19009 mastoxiv.page/@arXiv_csLG_bot/
- STDiff: A State Transition Diffusion Framework for Time Series Imputation in Industrial Systems
Gary Simethy, Daniel Ortiz-Arroyo, Petar Durdevic
arxiv.org/abs/2508.19011 mastoxiv.page/@arXiv_csLG_bot/
- EEGDM: Learning EEG Representation with Latent Diffusion Model
Shaocong Wang, Tong Liu, Yihan Li, Ming Li, Kairui Wen, Pei Yang, Wenqi Ji, Minjing Yu, Yong-Jin Liu
arxiv.org/abs/2508.20705 mastoxiv.page/@arXiv_csLG_bot/
- Data-Free Continual Learning of Server Models in Model-Heterogeneous Cloud-Device Collaboration
Xiao Zhang, Zengzhe Chen, Yuan Yuan, Yifei Zou, Fuzhen Zhuang, Wenyu Jiao, Yuke Wang, Dongxiao Yu
arxiv.org/abs/2509.25977 mastoxiv.page/@arXiv_csLG_bot/
- Fine-Tuning Masked Diffusion for Provable Self-Correction
Jaeyeon Kim, Seunggeun Kim, Taekyun Lee, David Z. Pan, Hyeji Kim, Sham Kakade, Sitan Chen
arxiv.org/abs/2510.01384 mastoxiv.page/@arXiv_csLG_bot/
- A Generic Machine Learning Framework for Radio Frequency Fingerprinting
Alex Hiles, Bashar I. Ahmad
arxiv.org/abs/2510.09775 mastoxiv.page/@arXiv_csLG_bot/
- ASecond-Order SpikingSSM for Wearables
Kartikay Agrawal, Abhijeet Vikram, Vedant Sharma, Vaishnavi Nagabhushana, Ayon Borthakur
arxiv.org/abs/2510.14386 mastoxiv.page/@arXiv_csLG_bot/
- Utility-Diversity Aware Online Batch Selection for LLM Supervised Fine-tuning
Heming Zou, Yixiu Mao, Yun Qu, Qi Wang, Xiangyang Ji
arxiv.org/abs/2510.16882 mastoxiv.page/@arXiv_csLG_bot/
- Seeing Structural Failure Before it Happens: An Image-Based Physics-Informed Neural Network (PINN...
Omer Jauhar Khan, Sudais Khan, Hafeez Anwar, Shahzeb Khan, Shams Ul Arifeen
arxiv.org/abs/2510.23117 mastoxiv.page/@arXiv_csLG_bot/
- Training Deep Physics-Informed Kolmogorov-Arnold Networks
Spyros Rigas, Fotios Anagnostopoulos, Michalis Papachristou, Georgios Alexandridis
arxiv.org/abs/2510.23501 mastoxiv.page/@arXiv_csLG_bot/
- Semi-Supervised Preference Optimization with Limited Feedback
Seonggyun Lee, Sungjun Lim, Seojin Park, Soeun Cheon, Kyungwoo Song
arxiv.org/abs/2511.00040 mastoxiv.page/@arXiv_csLG_bot/
- Towards Causal Market Simulators
Dennis Thumm, Luis Ontaneda Mijares
arxiv.org/abs/2511.04469 mastoxiv.page/@arXiv_csLG_bot/
- Incremental Generation is Necessary and Sufficient for Universality in Flow-Based Modelling
Hossein Rouhvarzi, Anastasis Kratsios
arxiv.org/abs/2511.09902 mastoxiv.page/@arXiv_csLG_bot/
- Optimizing Mixture of Block Attention
Guangxuan Xiao, Junxian Guo, Kasra Mazaheri, Song Han
arxiv.org/abs/2511.11571 mastoxiv.page/@arXiv_csLG_bot/
- Assessing Automated Fact-Checking for Medical LLM Responses with Knowledge Graphs
Shasha Zhou, Mingyu Huang, Jack Cole, Charles Britton, Ming Yin, Jan Wolber, Ke Li
arxiv.org/abs/2511.12817 mastoxiv.page/@arXiv_csLG_bot/
toXiv_bot_toot

@UP8@mastodon.social
2025-10-27 20:05:19

⏰ Smartphones manipulate our emotions and trigger our reflexes. No wonder we're addicted
theconversation.com/smartphone

Smartphones manipulate our emotions and trigger our reflexes — no wonder we’re addicted
The collective behaviours of smartphones — alerts, responsive interfaces, the use of gestures, the knowledge of intimate data — all feed into our reliance on the devices.